Evaluation of High-Resolution Digital Images for the 

Landscape Characterization of Colonia Settlements

By Richard D. Sanchez

Open-File Report 99-403

U.S. Department of the Interior 

U.S. Geological Survey
EVALUATION OF HIGH-RESOLUTION DIGITAL IMAGES FOR THE 

LANDSCAPE CHARACTERIZATION OF COLONIA SETTLEMENTS

Richard D. Sanchez

U.S. Geological Survey

521 National Center

Reston, VA20192

ABSTRACT

Colonia settlements, commonly described as rural unincorporated clusters of generally 

substandard houses, are scattered widely along the southern border of the United States. These 

settlements, which have proliferated without constraint largely because of the absence of effective 

controls on rural county land use, often lack access to essential services, such as public water, 

gas, electricity, telephone, and sewage disposal. A challenging problem in the southwestern 

border States is how to improve the living conditions of people residing in these settlements. 

Information about the infrastructure and geographic distribution of colonias will assist the 

Department of Housing and Urban Development (HUD) in making important decisions on 

funding grants and assistance programs for meeting the needs of these economically depressed 

areas. Consequently, the HUD Office of Research, Evaluation, and Monitoring, Washington, 

D.C., joined with the U.S. Geological Survey (USGS), Reston, Va., to evaluate the potential of 

forthcoming commercial high-resolution satellite images for the landscape characterization of 

colonia settlements.

Because the Government has high-resolution space sensor systems and commercial ones are 

planned, satellite images may offer a quick and effective way to gather information about 

economically depressed areas in the United States. Meanwhile, the USGS and HUD are 

investigating the applicability of high-resolution digital color images to identify and interpret 

colonia landscape characteristics. By creating simulated high-resolution 1-m digital color images 

derived from aerial cameras, this study examines the potential of future spaceborne orbital 

systems. Applying visual and automated interpretation techniques to high-resolution digital color 

images can distinguish between contemporary subdivisions with public utilities and colonias 

without utility structures. The research application of remote sensing, global positioning systems, 

and geographic information systems in this study yields important information on the usefulness 

and limits of forthcoming high-resolution spaceborne systems. The anticipated supply of 

commercially produced 1- to 4-m resolution satellite data will increase the information available 

to Federal agency users, but where much finer detail is needed, quality source data will have to 

come from commercial low-altitude aerial photographs or Government high-resolution satellite 

images. HUD will need these higher resolution sources to succeed in gathering accurate 

information about colonia settlements through remote sensing.

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement 

by the U.S. Government.
INTRODUCTION

Over the years, large quantities of satellite data available to the public have not been fully utilized 

because of a combination of limited markets for low-resolution data, national security restriction 

of high-resolution data, and lack of application awareness. With the national security restriction 

of high-resolution data reduced to 1 m (McDonald, 1995) and the advent of government and 

commercial high-resolution satellite systems, satellite images may become more useful in largescale geographic information system (GIS) development and maintenance (Seitz, 1995). In 

December 1998, Space Imaging, Inc., plans to launch a multisensor satellite with ground sample 

distances (GSD) of 1 m panchromatic and 4 m multispectral and an overflight repeat every 3 days. 

If all planned polar orbital space sensor systems are successfully launched by the year 2004, more 

than 20 satellites will be providing Earth resource data at GSD's of 1 m to 30 m in multispectral 

bands (Jensen, 1995). Several of these orbital space sensor systems can provide the requirements 

for large-scale image mapping from space. 1 Meanwhile, roughly simulated 1-m satellite color 

images generated from aerial photographs can be used to examine the value of future optimal data 

when they become available.

Colonia settlements, rural unincorporated clusters of generally substandard houses scattered 

widely along the southern border of the United States, often lack access to essential services such 

as public water, gas, electricity, telephone, and sewage disposal. According to the Texas Water 

Development Board, in 1996 an estimated 340,000 residents lived in 1,436 colonias situated in 

the State of Texas (Lyndon B. Johnson School of Public Affairs, 1996). Also, 32,000 people live 

in 35 colonias in Dona Ana County, New Mexico (Sullivan, 1994). These settlements are often 

located in isolated rural areas and require labor intensive and time consuming fieldwork to 

accurately detect and quantify (fig. 1). Information about living conditions and location of 

colonias can assist the Department of Housing and Urban Development (HUD) in making 

important decisions on funding grants and assistance programs for addressing infrastructure 

needs. In 1997, the HUD Office of Research, Evaluation, & Monitoring, Washington, D.C., 

joined with the U.S. Geological Survey (USGS), Reston, Va., in a pilot study to determine the 

feasibility of using remote sensing for gathering information on the landscape characteristics of 

colonia settlements.

Purpose of Study

The primary goal of this project is to evaluate the usefulness and limitations of 1-m GSD digital 

color images for detecting the landscape characteristics of colonia settlements. A second goal is 

to examine the usability of archived satellite data for measuring the changes and growth patterns 

in these settlements. The selection of public utility structures as the focus of landscape

1 "... reliable identification of cultural features, such as roads, railroads, and buildings that are normally shown 

on line maps at any scale, requires a ground resolution of 2- to 3-m/line pair, or better than a 1- to 2-m/pixel. If 

the sensor used for image mapping does not provide this level of resolution, cultural features will not be adequately 

represented unless they are derived from other source material." (Frederick J. Doyle, 1984)
characteristics in this study is based on the premise that landscape characteristics of colonia 

settlements are strongly indicated in part by the absence or scarcity of utility infrastructure, for 

example, waste, sewer, gas, and electricity.

Figure 1. Distribution of colonias settlements (triangles) along the U.S. side of the border.

The Project Area and Source Data

The project area is located between the rural-urban fringes of the standard metropolitan statistical 

areas of Las Cruces, N. Mex., and El Paso, Texas. Among the 33 designated colonia settlements 

in Dona Ana County, Vado IV and La Union were presented by the County Planning Office as 

sites for the study. The colonia settlement of Vado IV was chosen for the study because it more 

closely follows the Federal definition of a colonia. That of "a rural unincorporated subdivision in 

the United States near the Mexican border that lacks either a potable water supply, an adequate 

waste water system, adequate roads or drainage systems, or safe and sanitary housing" 

(Albuquerque Journal, 1996).
According to Holz and Davies (1992), colonias develop on plots of rural land, generally 10-20 

acres in size, but sometimes as small as 5 acres or as large as 400 acres. The 275-acre settlement 

of Vado IV was developed in the 1980's on three tracts of mostly barren land located off the U.S. 

Interstate 10 highway outside the corporate limits of Las Cruces and El Paso (fig. 2). Buildings in 

Vado IV vary in structural appearance, they contain a wide array of architectual designs, ranging 

from ramshackle shacks of differing sizes and materials to mobile homes, discarded buses, and 

houses of frame and brick construction. Outhouse privies are common, as well as piles of trash 

and building materials. Dismembered vehicles and rotting tires lie scattered throughout the 

settlement.

Figure 2. Location of Vado IV (inset of New Mexico shows project area) .

Physically, the project area is located within the Mesilla Basin, the southernmost of a series of 

basins along the Rio Grande in New Mexico. The basin is bounded by uplifted blocks of bedrock 

or by relatively impermeable volcanic rocks and is filled with alluvial sediment from the 

surrounding mountains and fluvial sediment carried by an ancestral Rio Grande (Frenzel and 

Kaehler, 1990).

The natural vegetation within this Chihuahuan desert climate region with its low precipitation of 

approximately 8.6 inches annual rainfall is limited to shrubby plants, such as creosote bush, yucca, 

agave, and sotol. Mesquite, cottonwood, and salt cedar can be found along the Rio Grande and 

irrigation canals, as well as other types of vegetation, such as alfalfa, cotton, fruit, and nut trees, 

which were introduced by early settlers.

4
The color-infrared (CIR) aerial photographs acquired for this project were collected under the 

USGS National Aerial Photography Program (NAPP) on March 3, 1996, at a flying height of 

6,096 m (20,000 ft) above mean terrain with a photographic scale of 1:40,000. The CIR 240- by 

240-mm (9- by 9-in) film positive was digitally scanned with an aperture of 20 microns yielding 

digital three-band image about 1-m GSD of approximately 432 megabytes. Also acquired were 

archived USGS digital orthophoto quadrangle (DOQ), digital elevation model (DEM) and NAPP 

black-and-white film negatives taken on February 5, 1991. NAPP aerial photographs are 

customarily collected in black and white using a 152.4-mm (6-in) focal length camera at an 

altitude of 6,096 m (20,000 ft). The resultant 240- by 240-mm film negative is scanned with an 

aperture of 25 microns, yielding a 1-m GSD digital one-band image of approximately 92 

megabytes. The satellite data acquired for this project were collected by Landsat 1 Multispectral 

scanner (MSS) on October 20, 1974, Landsat 4 thematic mapper (TM) on October 28, 1984, and 

Landsat 5 TM on October 29, 1996, under clear atmospheric conditions (Landsat MSS, Path 35, 

Row 37, and Landsat TM scenes, Path 33, Row 38). In this study, the data from the thermal 

band (TM band 6) with a GSD of about 120 m were not used. The project was also scheduled to 

evaluate data from the Space Imaging, Inc., satellite (1-m panchromatic, 4-m MSS) for validation 

of the simulated data results. However, the delays in the launch meant that this task was not 

possible within the timeframe of the study.

Preexisting spatial base data acquired for this study consist of USGS 1991 digital line graphs 

(DLG), 1982-84 geographic information retrieval analysis system (GIRAS) land use and land 

cover data, and 1991 AutoCAD parcel database from the Dona Ana County Planning Office. The 

USGS DLG data are digital representations of topographic and planimetric map features collected 

and updated in 1993 using the February 5, 1991, NAPP aerial photographs.

Map materials include USGS l:24,000-scale 7.5- by 7.5-minute topographic maps of La Mesa, N. 

Mex., La Union, N. Mex., and Canutillo, Texas/N. Mex. These USGS original topographic 

quadrangles were compiled or photorevised during the 1954-1974 period using a map projection 

and grid of the Universal Transverse Mercator (UTM), zone 13, with 1,000-m grid spacing, 

North American Datum (NAD) 27, and Clarke 1866 Spheroid.

METHODOLOGY

Public utility structures are the landscape feature characteristics on which this study will focus. A 

flow chart in figure 3 shows the process applied in this investigation. The methodology consists 

of the integration and analysis of 1-m digital images and geographic positioning system (GPS) 

based collected data. A second part of this study examines multidate Landsat MSS and TM 

sensor data for colonia growth trends and analysis.

To investigate the relationships between landscape characteristics of a colonia settlement and their 

interpretability in 1-m digital color imagery, we used the following approach. First a typical utility
network was identified from a nearby subdivision (herein referred to as Oneida) to develop as the 

base reference. This base reference served as a template to measure the interpretability and

NAPP and Satellite 

Data Acquisition

Image Preprocessing

Scanning 

Geometric Correction

Global Positioning 

System - Based 

Data Collection

Oneida Subdivision 

Public Utility Data

/ 1. Identification of 

/ Public Utility Features' 

>v and Attributes

/* Change & GrowthN. 

X^ Analysis using MultidateS. 

^xSatellite Images .s

Vado IV 

Public Utility Data

Integration of Spatial 

Information derived 

from Image of Vado IV 

using Oneida Model

Matrix-Overlay for

Feature Comparison

and Analysis

Figure 3. Flow process followed in this study.

information content of the 1-m digital imagery. The multilevel land use and land cover 

classification of the USGS system (Anderson et al., 1976) was adapted for use by modifying the 

Transportation, Communication and Utility categories to meet the needs of this investigation 

(table 1). Field visits to the project site were made in the summer of 1997 and 1998. A geocoded 

infrastructure GIS was produced of Oneida and Vado IV (using GPS-based collected data). Very 

little detailed, accurate geographic references for utility structures exist for Vado IV in the form 

of published or unpublished water, gas, wastewater, and stormwater block maps. County maps, 

although incomplete, were available of parcel lines, roads (no curb lines), and the water system.

Infrastructure GPS-Based Reference Development

The pre-onsite field procedure consisted of an inventory, collection, and evaluation of existing 

base data, followed by transforming useable data for export in a GIS compatible format, and
setting up the data dictionary and database tables (table 1). Important to the field strategy were 

planning data-logging routes in advance and defining function keys to capture feature attributes. 

Identifying National Geodetic Survey (NGS) High Accuracy Reference Network (HARN) 

locations in the vicinity of the project site proved invaluable for checking the accuracy of GPSdetermined positions at the beginning and end of the day (Sanchez and Hothem, 1998). A 

mission checklist was used to maximize time efficiency in the field.

Table 1. Utility GIS classification scheme

Level I Level II Level III Level IV

1 Built-up 14 Transport., 141 Roads 

Land Comm.,

& Utilities

147 Utilities

1411 Dirt/Graded

1412 Dirt/Ungraded

1413 Gravel

1414 Paved

1471 Catch Basin

1472 Electric Vault

1473 Fire Hydrant

1474 Gas Valve

1475 Light (private)

1476 Light (street)

1477 Manhole (electric)

1478 Manhole (gas)

1479 Manhole (storm sewer)

1480 Manhole (wastewater)

1481 Manhole (telephone)

1482 Pole (telegraph)

1483 Pole (utility)

1484 Water Valve

In the summer of 1997, the field team established a network of well-distributed, image-identified 

ground control points by real time differential GPS (RT-DGPS) using a Trimble Model 4000 SSE 

GPS receiver and OMNISTAR 6300A range-correction broadcast service. Both instruments 

were later replaced in the project by OMNISTAR Model 3000LR12~an integrated RT-DGPS 

system for GPS observations and reception of broadcasted range correction. The OMNISTAR 

Model 3000LR12 was cabled to a Micron laptop PC installed with GeoLink GPS/GIS mapping 

software. Augmenting the RT-DGPS capability was the application of the precise positioning 

service for point positions determined by autonomous methods with the Rockwell Precise 

Lightweight GPS + 96 Receiver (PLGR). The external antenna of the GPS receiver was 

magnetic-mounted on a field pack pole or on the roof of the vehicle. The GPS receiver and 

GeoLink interface allowed the laptop operator to display the digital image backdrop while 

recording an average position or logging the footpath or path of the vehicle continuously. The 

positional data referenced to NAD 83 were transmitted from the GPS receiver to GeoLink by
NMEA 0183 protocol. The GeoLink export program translated the captured data from its native 

storage format to ARC SHAPE, the target GIS file format.

The RT-DGPS system used in 1997 and 1998 produced differentially determined positions in realtime with horizontal accuracy at the sub-50-cm level and vertical accuracy at sub-70-cm level. 

The OMNISTAR service provides GPS range corrections collected at 11 widely spaced ground 

reference stations and transmitted to the user through a geostationary satellite communication 

link. The high quality range corrections provided by the OMNISTAR broadcast service are fully 

exploited by the internal 12-channel GPS engine of the OMNISTAR 3000LR12.

To know the precise grid coordinate of any point in the Oneida subdivision and Vado IV colonia 

settlement, the team performed registration in the field by locating 25 DGPS-collected ground 

control points within each of the 1996 scanned aerial photographs and correlating their horizontal 

position to corresponding geographic coordinates. These points were then used in a least-square 

regression analysis to derive a first-order polynomial transformation of the three band image with 

less than a 1-pixel root-mean-square error. Following the registration and nearest neighbor 

resampling at an output pixel size of 1 by 1 m, we used a high pass box filter to enhance higher 

spatial frequency components of the image. Because the study area is in a relatively flat area and 

mapped at a large scale, neither correction for terrain displacement nor orthorectification was 

deemed necessary for this study.

The GPS receiver and GeoLink field mapping software were used to collect utility structures and 

attributes. Most of the road data were collected with the GPS receiver's external antenna 

magnetic-mounted to the roof of the car, which was driven at speeds ranging from 5 to 35 

mph. The GeoLink interface with the GPS receiver allowed the operator to display the digital 

image and track the path of the vehicle. Because there were no heavy tree canopies, tall 

buildings, or other line-of-sight obstructions in the project area, the external antenna required less 

than 5 seconds to obtain a fix on the satellites. Once the field team obtained the fix on the 

satellites, tracking began at 1-second intervals. Stops were made to identify, collect attributes, 

and photograph features. Information about feature attributes was keyed into the computer 

simultaneously as GPS-determined coordinates were automatically collected. The digital camera 

image allowed the team to cross check the accuracy of the attribute data collected. The GeoLink 

laser range finder option enabled the field team to automatically georeference and calculate 

distance and bearing offsets of inaccessible utility structures, such as electric vaults. It took less 

than 5 minutes to enter attributes related to any utility structure. The descriptive data of utility 

features were collected by using predefined "hot keys" on the keyboard for different attributes (for 

example, manhole, type, and so on).

Once the data collection process was complete and results were inspected, the data were exported 

from GeoLink's native storage format (.DAT, .PLG .MDB and .IDB files) to the target GIS or 

ARC View interchange format (. SHP,. SHX, and .DBF files). During the export of the data to 

ARC SHAPE file format, they were cleaned by setting the GeoLink Translate Filters with an 

average cluster outlier tolerance of 10 to collapse small unwanted polygons (for example, double8
line roads, cul-de-sacs, and so on) and the weed filter of 10 m to remove unnecessary points. 

Setting the ARCView file extension to ERDAS Imagine Image support allowed the rectified 

images to be viewed as backdrops to the ARC SHAPE files. For example, electric vault ARC 

SHAPE files were superimposed on the ERDAS .img file. Color and black-and-white ARCView 

graphic and tabular information was printed on a Canon BJ80 portable printer to supplement the 

verification process.

Each spatial base overlay was individually viewed in contrasting colors over the image and 

systematically inspected for proper fit (fig. 4). The ability to switch overlays and toggle to adjust 

the ARCView color display provided a quick visual check of the registration and any spatial 

inconsistencies. A random sampling was performed by systematically reviewing a vector display 

of the data set and image backdrop in subsections. Any feature that appeared to be either missing, 

altered, or unidentifiable in the image was marked for further inspection.

Figure 4. Comparison of 1-m GSD image at 1:1,500 scale of Oneida with and without database 

reference overlay showing location of manholes and electric vaults.

ANALYSIS

Utility Structures

By using the field-acquired GPS-based referenced data, the field team conducted a detectability 

test of the utility structures in the 1-m digital color images. On the computer screen, all relevant 

utility features that could be identified and interpreted were systematically plotted as vector 

overlays on the l:9,000-scale image backdrop. Afterward, each newly plotted feature overlay
was compared with its corresponding base reference data layer displayed in a second viewer. The 

ability to zoom, toggle, and combine overlays provided a quick indication of the features that 

were not captured. Any feature that appeared to be either missing or unidentifiable in the test was 

marked for further inspection.

Accuracy Assessment

The team checked the accuracy of the newly plotted feature layers of Oneida and Vado by 

comparing them to their corresponding layer from the base reference data (collected, annotated 

and edited in the field) and the 1-m digital color image. This accuracy is defined as the proportion 

correctly identified and interpreted on the 1 m digital image when compared to the ground truths 

established from the base reference data gathered in the field (Sturdevant, 1981). An additional 

cross-check was made to verify that the entered data were correctly matched to the feature seen 

in the stored digital photograph.

The accuracy of the utility structures identified and interpreted in the Oneida and Vado IV images 

can be seen in tables 2 and 3, respectively. The high percentage totals for Vado IV reflect 

building and road features. The lack of or scarcity of public utilities in Vado IV is also evident; 

the only missing utility feature, a water valve, was observed on Tarin Road near the Ward Street 

intersection.

Table 2. Overlay matrix and summary statistics for Oneida 1 m pixel resolution image

Features;

Percentage of Feature: 

Bides Rds Ev Mh Wv Missing TOTAL Identified Interpreted

Buildings 164

Roads 10

Utilities

Evaults (Ev) 0

Manholes (Mh) 0

Watervalve (Wv) 0

2

0

19

17

20

166

10

19

17

20

98.8

100

0

0

0

98.8

100

0

0

0

TOTAL 164 10 000 58 232 75.0 75.0

10
Table 3. Overlay matrix and summary statistics for Vado IV 1 m pixel resolution image

Percentage of Feature: 

Features: Bldgs Rds Ev Mh Wv Missing TOTAL Identified Interpreted

Buildings 129

Roads 18

Utilities

Evaults (Ev) 0

Manholes (Mh) 0

Watervalve Wv) 0

8

1

0

0

1

137

19

0

0

1

94.2

94.7

0

0

0

94.2

94.7

0

0

0

TOTAL 129 18 0 0 0 10 157 93.6 93.6

Because a higher resolution color image of the study area was not readily available, a sample 

l:7,500-scale color photograph of the Ft. Dupont area, Washington, D.C., scanned at a 20-cm 

pixel resolution was used for comparison (figure 5). This 20-cm sampled image was derived from

Figure 5. Example of manhole covers in 20-cm pixel resolution image, see arrows.

11
aerial photographs flown for the National Capitol Planning Commission and was obtained from 

the National Park Service. In table 4, the summary statistics for 20-cm imagery show the 

identification and interpretation significantly higher for the same utility features.

Table 4. Comparison statistics for sample 20 cm pixel resolution image

Percentage of Feature: 

Features: Bides Rds Ev Mh Wv Missing TOTAL Identified Interpreted

Buildings 48

Roads 5

Utilities

Evaults (Ev) 9

Manholes (Mh)

Watervalves (Wv)

0

0

1

15 2

0 20

48

5

10

17

20

100

100

90

88.2

0

100

100

90

88.2

0

TOTAL 48 15 23 100 77 77

The low identification and interpretation results in tables 2 and 3 as compared with those of Table 

4 demonstrate that 1 m GSD imagery is unlikely to provide a viable alternative to 20-cm pixel 

resolution for developing an infrastructure-type GIS. An evaluation with stereoimagery was not 

done here, but should show a significant improvement in identification and interpretation at 1-m 

and 20-cm GSD. However, the same trend is expected (that is, buildings and road features more 

recognizable than the utility structures).

MONITORING CHANGES AND GROWTH

Monitoring change and growth in colonia settlements is a natural extension of current research in 

developing landscape characteristics or "footprints" of these settlements from high-resolution 

digital imagery. The image mapping of physical surface change, trends, and growth pattern has 

traditionally been accomplished with coarse 20- to 30- m satellite sensors from four-band SPOT 

and six-band Landsat (thermal band not included) multispectral data, respectively. Because the 

equivalent multispectral satellite data at a high resolution were not available, contemporary source 

data and techniques were applied in this phase of the project. Further research is recommended 

when planned satellite systems with four-band, multispectral, 4-m GSD data become available.

Satellite Image Preparation

Satellite image preparation for the study consisted of processing four digital data sets in raster 

format from 1974, 1984, 1994, and 1996 Landsat scenes. Rectification and resampling were

12
performed by locating the same 15 well-distributed photoidentified control points in each image 

and correlating their x and y positions to corresponding UTM coordinates. These points were 

then used in a least-square regression analysis to derive a first-order polynomial transformation of 

each image with less than a 1-pixel root-mean-square error. The MSS and TM data were 

resampled at a pixel size of 30 m (MSS at 60 m) to a UTM projection, zone 13, NAD 83, and 

GRS 80. Following the registration and nearest neighbor resampling of the Landsat data, a highpass box filter was used to enhance the higher frequency spatial components of the image. The 

mapped area of each data set was then viewed in subsections to permit a quick, systematic 

inspection of the raster image display.

The team acquired supplementary source material in the form of sample spectra using groundtruthing field spectrometry techniques. A 350- to 2,500-nm full range of the Analytical Spectral 

Device (ASD) FieldSpec Spectrometer was used to analyze spectra groups in the project. The 

field team took measurements between 10 a.m. and 3 p.m. when the sun was at its highest above 

the horizon. Each target measurement was taken from a height of approximately 1-meter over the 

spectra, aiming vertically facing the sun. The instrument operator calibrated the spectrometer 

often to compensate for subtle changes in atmospheric humidity, sun angle, high-altitude haze, 

and dust that can introduce unwanted characteristics into the sample spectra.

Change Analysis

Several common visual and automated techniques used for identifying and interpreting physical 

change, trends, and growth pattern include (1) multidate visual comparison, (2) single-band 

change detection, (3) image differencing, (4) image regression, and (5) classification comparison. 

Multidate visual comparison uses monochromatic and stereophotos and airphoto interpretation 

techniques (shape, size, pattern, and so on) to identify changes over time. In single-band change 

detection, an image data set is created from multidate images, and various band color 

combinations are displayed to reflect variation in spectral response over time. Image differencing 

uses two registered satellite images, where radiance values from one date are subtracted from the 

corresponding location of the second date (Toll, 1980). The image-regression approach is a 

variation of the image-differencing technique whereby a least-squares transformation between 

dates is used to reduce adverse effects from differences in atmospheric conditions or sun angles. 

The fifth technique applies the classification of multispectral images for two dates then compares 

the classifications for nonurban to urban land cover change (referred to by Toll as 

postclassification comparison).

For the single-band change detection, a 30-m output pixel was selected to enable integration of 

the MSS and TM bands into a single-band data set. A single-band change detection image was 

created by comparing differences in the images' brightness values or digital numbers of TM band 7 

(for the 1974 data, MSS band 4 was used). TM Band 7 (2.08-2.35 urn) and MSS band 4 (0.8- 

1.1 um) were used because they enable the delineation of urban areas from nonurban areas and, in 

the case of TM band 7, penetrate haze in the atmosphere. TM bands 5 (1.55-1.75 um) and 7 have

13
demonstrated operational value in the midinfrared for cultural delineation. However, because the 

two bands have high correlation, only one was used (Chavez, 1984).

In creating the change image, we assigned blue and green to the brightness values in the 1984 

image and red to the brightness values in the 1996 image. The brightness values that have the 

same red, green, and blue intensities for both dates are shades of gray and will remain unchanged. 

The brightness values that were brighter in 1984 become shades of cyan because they are high in 

blue and green values and low in red. Conversely, the brightness values that were brighter in 

1996 become shades of red because they are high in red and low in blue and green values. 

Therefore, cyan represents higher brightness or reflectance values in the 1984 display, and red 

represents higher reflectance values in the 1996 display. In the visual interpretation, intense 

shades of red and cyan, which result from the high amount of change between the 1984 and 1996 

brightness values, indicate physical surface changes in the project area.

After the change scenes were created, showing changes between 1974 and 1984, image band and 

color combinations were generated to further analyze and classify the amounts and types of 

changes. Images of the three dates were generated using TM bands 7, 4, 2, and MSS 4, 3, 2, in 

red, green, and blue, respectively. This band and color combination delineates built-up land areas 

and differentiates vegetated and nonvegetated areas. In the 1974 image in figure 2, pink shows 

barren soil and white reveals nonvegetated areas or areas that have been cleared for construction. 

As anticipated, comparison of multitemporal images shows most changes taking place in the 

rural-urban fringes where most colonia settlements are located. The white areas in the 1974 and 

1984 images have changed to bright green in the 1996 image, indicating a change from a cleared 

area to a built-up land area with vegetation. In the change image, such areas appear as bright 

cyan, which accurately denotes regions of large differences in spectral response between 1974, 

1984, and 1996. On the basis of visual interpretation of the change images and the three Landsat 

scenes, it is possible to predict that the white areas in the 1996 image have been, or will soon be, 

Andersen's Level I Built-up Land areas, such as colonia settlements.

The potential of field spectrometry for the classification and mapping of surface change was not 

evaluated in this study. Because of the significance of spectral information, a separate 

investigation of its value to landscape change analysis should prove worthwhile.

FINDINGS

The detectability test results for the 1-m image revealed that most infrastructure features, with the 

exception of certain utility structures, could be observed at Anderson Levels I and II land use/land 

cover classification, and to some extent at a Level III classification (table 5). In general, when the 

images were displayed in black and white, they provided high detectability, but when they were 

displayed in color a greater level of interpretability was achieved. On the Vado IV image, waterrelated features, such as arroyos, drains, and irrigation canals, were easily detectable. Clearly 

recognizable were open fields, houses, highways (Interstate 10 and MM 227), and unpaved roads 

(there are no paved roads in Vado IV). Manmade features, such as level of unimproved roads

14
(for example, ungraded, graded, or gravel), trails, and narrow footpaths, may require verification 

in the field. Small houses, mobile homes, discarded buses, ramshackle shacks, outhouse or 

privies, dismembered vehicles, and piles of rotting tires, trash and building materials, were easy to 

identify, but difficult to interpret (for example, houses of frame and brick construction). The 

identification of features such as levees or banks may require stereoimages or contour overlays.

Table 5. Detectability test results at 1-m pixel resolution

Features Class Capture Results

Roads and Trails

Utilities

Hydrography

Manmade

Levels 1, 2, 3 Easily recognizable in open areas, 

especially in color

Levels 3, 4 in trees Stereoimages essential

Level 4 Paved/unpaved distinguishable in 

open areas; difficult to classify 

graded/gravel/ungraded w/o 

supplemental data

Levels 3, 4 

Drains, arroyos

Difficult to impossible to capture w/o 

higher resolution data or field survey

Recognizable in open areas, better 

in color

Small houses, lots, Recognizable in open areas, better 

mobile homes in color

Transmission/Pipelines Transmission towers Only if ground scar is visible, better 

in color

Water pipelines Only if above ground and scar visible, 

better in color

On the sample 20-cm GSD color image of Ft. Dupont, Washington, D.C., most utility structures 

were detectable and general identification was possible. However, manhole covers were more 

recognizable on concrete pavement than on asphalt, and smaller water valve covers were not 

detectable on either. Based on these detectability tests results, the detection and precise 

identification of Anderson Level IV utility structures will require images with less than 1-m GSD.

15
Aerial photographs and satellite images are important to the feature identification process. 

However, processing such as digitizing and resampling can degrade the data, regardless of 

enhancement procedures. Further investigation into preserving the spatial and spectral fidelity 

after processing is recommended.

In the process of tiling or mosaicking, we found that each scanned aerial photograph has an 

associated accuracy that results in minor edge-matching problems. In an aerial photograph, tall 

objects appear to lean outward from the photograph's center. With orthophoto rectification, most 

of this distortion is correctable; however, large features near the photograph's edge that are 

dissected by the mosaic line may still appear distorted or displaced. For example, where a wide 

drain like Mesquite Drain runs between adjoining images, there may be relative displacement and 

mismatching. Also, both sides (for example, west and east sides) of a large building dissected by 

the mosaic line may be erroneously visible, as well as their shadows. Most of these displacements 

or distortions can be selectively smoothed during image processing, but they cannot always be 

positionally corrected. For high-resolution digital imagery, this particular problem may be 

reduced or eliminated in the near future, since orbital sensor systems generally have a narrowangle field-of-view, are near orthographic, and will basically eliminate many of the anomalies of 

the aerial camera (ISPRS Committee, 1984). However, for aerial photographs with better than 1- 

m ground resolution, further research on displaced feature problems along the mosaic line is 

recommended.

The multidate satellite integrated data sets permitted image analysis and interpretation in several 

different parts of the visible to midinfrared spectrum at scales of up to 1:50,000. The single band 

change detection image provided better contrast and separation, making it easier to correctly 

identify physical surface alteration. Changes in manmade features were the most difficult to 

recognize from the satellite images and benefited the most from integration with newly created or 

existing spatial base data. Archived coarse or low-resolution multispectral satellite images alone 

may not be able to accurately determine the growth or change in colonia settlements. Adding 

another dimension to the potential of integration is the fusion of low-resolution multispectral data 

with archived source data from U.S. Government declassified high-resolution reconnaissance 

images from space (McDonald, 1995).

With the anticipated launch of a Space Imaging, Inc., satellite in December 1998, and other 

similar commercial launches over the next few years, satellites will provide multispectral sensor 

data with a pixel resolution of 4 m or better. These new sources of high-resolution multispectral 

data should increase the capabilities for generating landscape change information at local scales 

with greater geometric detail and accuracy. This project did not examine the potential of 4-m 

multispectral satellite data for temporal analysis. Because of the significance of temporal 

information for calibration, correction, and convergence of evidence for colonia growth trends, 

this topic deserves a complete study of its own.

The findings suggest that by using relatively large-scale (submeter GSD) and recently acquired 

digital images, scientists can identify economically depressed settlements like colonia settlements,

16
as well as their level of infrastructure. This can be done at an 85 percent accuracy rate, or even 

higher if the digital images are supplemented or integrated with high-accuracy, georeferenced 

spatial field data.

The sample 20-cm GSD color image used in this study demonstrates that higher resolution is 

necessary to identify and interpret the infrastructure characteristics in the form of public utility 

structures. The simulated 1-m color image fell short in capturing most of these same 

characteristics. Public utility features, or their absence, are vital to the identification of colonia 

settlements and their levels of infrastructure. Because of the poor results, we can conclude that 

forthcoming 1-m GSD satellite imagery is unlikely to provide a viable alternative to low-altitude 

aerial photographs and high-resolution reconnaissance satellite images for the accurate (1) 

identification of colonia settlements, (2) assessment of settlement patterns, population estimates, 

housing, and living conditions, and (3) development of a prototype data set.

CONCLUSION

The integration of better than 1-m GSD images and high-accuracy, georeferenced, field-collected 

spatial data in an infrastructure GIS database can provide a highly flexible tool for documenting 

the level of public utilities in colonia settlements. Because of the absence of real satellite imagery 

with 1-m panchromatic and 4-m multispectral GSD, the digitized aerial photographic data were 

used instead, and only simulated the pixel GSD. It is important to keep in mind the limitations of 

current and future satellite image integration with spatial base data, and to balance the criteria for 

its use against practical considerations of designing a colonia settlement GIS database. Pixel size, 

ground control, topography, and accuracy of the DEM govern the factors determining suitability 

of digital images for a given application. Digital images at 1-meter GSD from commercial 

satellites and, in most cases, commercially available coarse OEM's will not be suitable for largescale projects requiring pixel footprints of utility structures with submeter positional accuracy 

(table 6).

Another factor to consider is that, unless the essential topographic data are digitized or generated 

from planned orbital space-imaging systems capable of mapping in flat and rough terrain, orbital 

sensor data available for a colonia settlement GIS database may be limited to digital planimetric 

base data. Studies of the use of satellite data for surveying and mapping indicate that 5-m 

contours can be derived from 2.5-m pixel systems suitable to produce a l:25,000-scale line map 

where a satisfactory base-to-height ratio and adequate geometric constraints are imposed 

(Frederick J. Doyle, 1984, unpub. data). Theoretically, SPOT 3 and planned orbital systems 

identified in the PE&RS Journal (Jensen, 1995), such as Spacelmaging, can be used for 

topographic mapping but only after extensive image processing to correct the stereomodel (A. 

Colvocoresses, oral cornmun. August 14, 1995). The problem lies within the movement of the 

sensor during the pass to collect the fore and aft images needed for stereopairs that "disrupts 

continuity and complicates the geometric solution of the stereomodel" (Fester, 1995). Possible 

exceptions are EarthWatch and SPOT 5 systems. SPOT 5 stereoscopic configuration is similar in 

design to the USGS's MAPS AT (Colvocoresses, 1982). Both EarthWatch and SPOT 5 systems

17
are designed to limit the movement of the sensor and to reduce the discontinuity of orbit, thus 

permitting straightforward geometric solution of the stereomodel. These two exceptions will 

provide continuous real time data and should meet most of the parameters for an orbital mapping 

system recommended by the ISPRS Committee, Commission IV Report (1984).

Table 6. Detectability requirements as they relate to developing infrastructure GIS 

through remote sensing (values are GSD or pixel equivalents in metric units)

Feature

Roads & Trails

Catch Basin

Electric Vault

Fire Hydrant

Gas Valve

Light (private)

Light (street)

Manhole (electric)

Manhole (gas)

Manhole (storm sewer)

Manhole (waste water)

Manhole (telephone)

Pole (telegraph)

Pole (utility)

Water Valve

Drains/ditches

Houses/mobile homes

Detection

9m

90cm

90cm

30cm

15 cm

15 cm

15 cm

60 cm

60 cm

60 cm

60 cm

60 cm

30cm

30cm

15 cm

4 m

2m

General I.D.

6 m

50cm

50cm

15 cm

7 cm

7 cm

7 cm

20cm

20cm

20cm

20cm

20cm

15 cm

15 cm

7 cm

2m

1m

Precise I.D.

2m

25 cm

25 cm

7 cm

3 cm

3 cm

3 cm

10cm

10cm

10cm

10cm

10cm

7cm

7 cm

3 cm

1m

50cm

Attribute I.D.

60 cm

15 cm

15 cm

3 cm

1 cm

1 cm

1 cm

5 cm

5 cm

5 cm

5 cm

5 cm

3 cm

3 cm

1 cm

50cm

25 cm

The anticipated supply of commercially produced 1- to 4-m GSD satellite data will increase the 

data available to Federal agency users, but where much finer detail is needed, quality source data 

will have to come from commercially flown low-altitude aerial photographs or higher resolution 

Government satellite images. These higher resolution sources of digital images will be essential to 

HUD for determining the level of infrastructure of colonia settlements through remote sensing.

18
ACKNOWLEDGMENTS

The author would like to thank Larry Hothem, USGS, Michael E. Slifer, USGS Water Resource 

Division, Las Cruces Subdistrict Office, and Judy Price, Dona Ana County Planning Office, for 

their support and contributions to this project. The U.S. Department of Housing and Urban 

Development provided part of the funding for this project.

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